SEARCH · Search NASA

Results for “measurement error model”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3

All sources NASA NTRS DOE OSTI

NASA NTRS2014

A Regional CO2 Observing System Simulation Experiment for the ASCENDS Satellite Mission

Top-down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the WRF-STILT Lagrangian transport model in a regional Bayesian synthesis inversion. The regional Lagrangian framework is well suited to make use of ASCENDS observations to constrain fluxes at high resolution, in this case at 1 degree latitude x 1 degree longitude and weekly for North America. We consider random measurement errors only, modeled as a function of mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the 1 degree x 1 degree, weekly scale, the largest uncertainty reductions, on the order of 50 percent, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 micron candidate wavelength than for a 2.05 micron wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual, biome scale range from 40 percent to 75 percent across our four instrument design cases, and from 65 percent to 85 percent for the continent as a whole. Our uncertainty reductions at various scales are substantially smaller than those from a global ASCENDS inversion on a coarser grid, demonstrating how quantitative results can depend on inversion methodology. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr-1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

remote sensing↗

NASA NTRS1986

Modeling Pluto-Charon mutual eclipse events. I - First-order models

The present 'first order' analytical and numerical models of light curves due to mutual events between close planetary binaries, the effects of shadowing are included. Attention is given to the case of the Pluto-Charon system. The results of the analytical and numerical approaches agree to well within the expected light curve measurement error. The model predicts that the current mutual eclipse event series will end by November 1990.

Dunbar, R. Scott↗

NASA NTRS2011

A Maximum Likelihood Approach to Determine Sensor Radiometric Response Coefficients for NPP VIIRS Reflective Solar Bands

Optical sensors aboard Earth orbiting satellites such as the next generation Visible/Infrared Imager/Radiometer Suite (VIIRS) assume that the sensors radiometric response in the Reflective Solar Bands (RSB) is described by a quadratic polynomial, in relating the aperture spectral radiance to the sensor Digital Number (DN) readout. For VIIRS Flight Unit 1, the coefficients are to be determined before launch by an attenuation method, although the linear coefficient will be further determined on-orbit through observing the Solar Diffuser. In determining the quadratic polynomial coefficients by the attenuation method, a Maximum Likelihood approach is applied in carrying out the least-squares procedure. Crucial to the Maximum Likelihood least-squares procedure is the computation of the weight. The weight not only has a contribution from the noise of the sensor s digital count, with an important contribution from digitization error, but also is affected heavily by the mathematical expression used to predict the value of the dependent variable, because both the independent and the dependent variables contain random noise. In addition, model errors have a major impact on the uncertainties of the coefficients. The Maximum Likelihood approach demonstrates the inadequacy of the attenuation method model with a quadratic polynomial for the retrieved spectral radiance. We show that using the inadequate model dramatically increases the uncertainties of the coefficients. We compute the coefficient values and their uncertainties, considering both measurement and model errors.

Lei, Ning↗

NASA NTRSDate not supplied

Data Fusion of In-Flight Aerothermodynamic Heating Measurements Using Kalman Filtering

On February 18th, 2021, the Mars 2020 entry system successfully delivered the Perserverance rover to the surface of Mars at Jezero Crater. The entry capsule carried a set of instrumentation installed on the heat shield and backshell, named the Mars 2020 Entry, Descent, and Landing Instrumentation. The instruments include pressure transducers, thermocouples, heat flux gauges, and radiometers to measure the aerodynamic and aerothermodynamic performance of the entry vehicle. Three of these sensors, a thermocouple plug, heat flux gauge, and a radiometer, are located in close proximity on the backshell. Each sensor is exposed to roughly the same environment, but measured these environments in different ways, each with its own set of modeling and measurement error complications. This paper develops a method for blending each of these measurements together in a single algorithm to produce estimates of the aerothermodynamic environments at that location on the backshell. The approach makes use of the Kalman filter methodology for solving state estimation problems. The filter has a predictor/corrector structure in which simplified process models are used to propagate the aeroheating states forward through time, which are then updated based on the measurement data. The method has been tested on simulated datasets.

Christopher D. Karlgaard↗

NASA NTRSDate not supplied

Dose Error Correction Using Simulation Extrapolation for Community Noise Dose-Response Modeling

The objective of this work is to provide a framework to account and correct for dose error in dose-response modeling due to measurement uncertainty. Error in noise measurements, especially in the case of limited monitoring locations in a community, can lead to an attenuation or misestimation of parameters in dose-response models. This error can result in overpredicted annoyance at lower doses and underpredicted annoyance at higher doses. Simulated data in the present work are based on previous NASA community studies and incorporate a notional design for future studies with the X-59 aircraft. Several populations of different annoyance response sensitivities are included. Simulation extrapolation (SIMEX, Cook and Stefanski 1994) is used to correct for the dose error in a simple, fully pooled logistic regression. Results indicate the negative impact of attenuation is greatly diminished for all amounts of dose error considered, regardless of a population’s annoyance sensitivity. Therefore, SIMEX can help produce a more accurate dose-response relationship.

SIMEX↗

NASA NTRS1974

Parameter estimation for terrain modeling from gradient data

A method is developed for modeling terrain surfaces for use on an unmanned Martian roving vehicle. The modeling procedure employs a two-step process which uses gradient as well as height data in order to improve the accuracy of the model's gradient. Least square approximation is used in order to stochastically determine the parameters which describe the modeled surface. A complete error analysis of the modeling procedure is included which determines the effect of instrumental measurement errors on the model's accuracy. Computer simulation is used as a means of testing the entire modeling process which includes the acquisition of data points, the two-step modeling process and the error analysis. Finally, to illustrate the procedure, a numerical example is included.

Dangelo, K. R.↗

NASA NTRS2019

Evaluation Analysis of NASA SMAP L3 and L4 and SPoRT-LIS Soil Moisture Data in the United States

Soil moisture has a critical role in the development, frequency and persistence of climatic and hydrologic extremes such as drought, heat wave and flooding events. In situ soil moisture data are uneven and sparse in time and space. This highlights the need to utilize other soil moisture sources to fill this spatiotemporal gap. The goal of this study is to validate one satellite-based and two model-based soil moisture datasets with in situ data across the United States. Soil moisture information from the Soil Moisture Active Passive (SMAP) enhanced level 3 (L3) (SMAP L3) and modeled level 4 (L4) (SMAP L4) data at 9-km resolution and Short-term Prediction Research and Transition-Land Information System (SPoRT-LIS) modeled at 3-km resolution were selected for evaluation. SPoRT-LIS is a near real-time, high resolution operational land analysis data. Ground-based data were obtained from the North American Soil Moisture Database (NASMD) for 362 stations. Seven statistical indicators including anomaly, Spearman, and Pearson correlation coefficients, the systematic error (Bias), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and normalized standard deviation (SDV) were used to evaluate the satellite- and model-derived soil moisture data. In addition, the triple collocation (TC) error model was used to measure the error among SMAP L4, SPoRT-LIS and ground-based data. This study assesses which satellite or modeled dataset is most appropriate for specific times and locations to use as a surrogate for in situ observations. Temporal and spatial analysis demonstrated that, overall, SMAP L4 performed better than SMAP L3 and SPoRT-LIS. Strong agreement was observed between SMAP L4 and in situ observations (ρ = 0.53, Bias = −0.006) in all seasons and most regions with various land covers, especially in winter and in the central regions of the United States. For croplands, SMAP L4 presented the best agreement with in situ data, analyzing all period (ρ = 0.60) and non-winter period (ρ = 0.61) separately.

Tavakol, Ameneh↗

NASA NTRS2017

Joint Assimilation of SMOS Brightness Temperature and GRACE Terrestrial Water Storage Observations for Improved Soil Moisture Estimation

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0 - 5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.

Soil Moisture↗

NASA NTRS2024

Effective structural impact detection and localization using convolutional neural network and Bayesian information fusion with limited sensors

Due to their unpredictable nature, many impact events (e.g., overheight vehicles striking on bridges) go unnoticed or get reported many hours later. However, they can induce structural failures or hidden damage that accelerates the structure’s long-term degradation. Therefore, prompt impact detection and localization strategies are essential for early warning of impact events and rapid maintenance of structures. Most existing impact detection strategies are developed for aircraft composite panels utilizing high-rate synchronized measurement from densely deployed sensors. Limited efforts have been made for infrastructure or human habitats which generally require large-scale but low-rate measurement. In particular, due to harsh environments (e.g., deep space habitats under meteoroids), structural impact localization must be robust to limited sensors (e.g., sensor damage during impacts) and multi-source errors (e.g., measurement errors). In this study, an effective impact detection and localization strategy is proposed using a limited number of vibration measurements, especially in harsh environments (e.g. in deep space). Convolutional neural networks are trained for each sensor node and are fused using Bayesian theory to improve the accuracy of impact localization. Special considerations are paid to evaluate the effect of both measurement error and modeling error in the analysis. The proposed strategy is illustrated using 1D structure, and further validated in 3D geodesic dome structure numerically. The results demonstrate that it can detect and localize impact events accurately and robustly on structures.

Yuguang Fu↗

NASA NTRS1980

The effect of directional radiation models on the interpretation of earth radiation budget measurements

A parameter estimation technique is presented to estimate the radiative flux density distribution over the earth from a set of radiometer measurements at satellite altitude. The technique analyzes measurements from a wide field of view, horizon to horizon, nadir pointing sensor with a mathematical technique to derive the radiative flux density estimates at the top of the atmosphere for resolution elements smaller than the sensor field of view. A computer simulation of the data analysis technique is presented for both earth-emitted and reflected radiation. The errors resulting from the assumed directional radiation model, spatial model and random measurement error have little effect on the global mean radiation. Zonal estimates were found to be more sensitive to the spatial model than to the directional radiation model. Results from analyzing medium field of view measurements showed a much greater sensitivity to the directional radiation model even on a global scale.

Green, R. N.↗

NASA NTRS1993

Postlaunch calibration of spacecraft attitude instruments

The accuracy of both onboard and ground attitude determination can be significantly enhanced by calibrating spacecraft attitude instruments (sensors) after launch. Although attitude sensors are accurately calibrated before launch, the stresses of launch and the space environment inevitably cause changes in sensor parameters. During the mission, these parameters may continue to drift requiring repeated on-orbit calibrations. The goal of attitude sensor calibration is to reduce the systematic errors in the measurement models. There are two stages at which systematic errors may enter. The first occurs in the conversion of sensor output into an observation vector in the sensor frame. The second occurs in the transformation of the vector from the sensor frame to the spacecraft attitude reference frame. This paper presents postlaunch alignment and transfer function calibration of the attitude sensors for the Compton Gamma Ray Observatory (GRO), the Upper Atmosphere Research Satellite (UARS), and the Extreme Ultraviolet Explorer (EUVE).

Davis, W.↗

NASA NTRS1997

System Dynamic Analysis of a Wind Tunnel Model with Applications to Improve Aerodynamic Data Quality

The research investigates the effect of wind tunnel model system dynamics on measured aerodynamic data. During wind tunnel tests designed to obtain lift and drag data, the required aerodynamic measurements are the steady-state balance forces and moments, pressures, and model attitude. However, the wind tunnel model system can be subjected to unsteady aerodynamic and inertial loads which result in oscillatory translations and angular rotations. The steady-state force balance and inertial model attitude measurements are obtained by filtering and averaging data taken during conditions of high model vibrations. The main goals of this research are to characterize the effects of model system dynamics on the measured steady-state aerodynamic data and develop a correction technique to compensate for dynamically induced errors. Equations of motion are formulated for the dynamic response of the model system subjected to arbitrary aerodynamic and inertial inputs. The resulting modal model is examined to study the effects of the model system dynamic response on the aerodynamic data. In particular, the equations of motion are used to describe the effect of dynamics on the inertial model attitude, or angle of attack, measurement system that is used routinely at the NASA Langley Research Center and other wind tunnel facilities throughout the world. This activity was prompted by the inertial model attitude sensor response observed during high levels of model vibration while testing in the National Transonic Facility at the NASA Langley Research Center. The inertial attitude sensor cannot distinguish between the gravitational acceleration and centrifugal accelerations associated with wind tunnel model system vibration, which results in a model attitude measurement bias error. Bias errors over an order of magnitude greater than the required device accuracy were found in the inertial model attitude measurements during dynamic testing of two model systems. Based on a theoretical modal approach, a method using measured vibration amplitudes and measured or calculated modal characteristics of the model system is developed to correct for dynamic bias errors in the model attitude measurements. The correction method is verified through dynamic response tests on two model systems and actual wind tunnel test data.

Buehrle, Ralph David↗

NASA NTRSDate not supplied

Estimation With Range Dependent Sensor Model

This paper focuses on the improvement of target tracking accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. We present two approaches to tracking a target with constant velocity model. The first approach uses Extended Kalman Filter (EKF) framework to estimate target's states and the unknown proportionality coefficient with linearization on the observation model on the predicted states at each time step. The second one uses a measurement conversion method to estimate the target's scaled by the unknown constant term states, which is shown to be unbiased. This conversion results in linear state and observation models, hence the standard Kalman filter can be applied. The actual target state is computed by application of back scaling with the estimate of unknown scale factor. We evaluate the approaches in desktop simulations.

Target tracking↗

NASA NTRSDate not supplied

Estimation With Range Depended Sensor Model

This paper focuses on the improvement of object detection accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. The tracking problem is considered in a mixed continuous-discrete time domain, where the target dynamics is in continuous-time and the measurements are in discrete-time, which is an optimal choice in many tracking and navigation applications. We adopt a commonly used continuous time coordinate-uncoupled white-noise acceleration model for a point object to describe the target motion, and use Extended Kalman Filter (EKF) framework to estimate the target's state and the unknown proportionality coefficient.

Vahram Stepanyan↗

NASA NTRS2019

The Badhwar-O'Neill 2020 Model

The Badhwar-O'Neill (BON) model has been used for some time to describe the galactic cosmic ray (GCR) environment encountered in deep space by astronauts and sensitive electronics. The most recent version of the model, BON2014, was calibrated to available measurements to reduce model errors for particles and energies of significance to astronaut exposure. Although subsequent studies showed the model to be reasonably accurate for such applications, modifications to the sunspot number (SSN) classification system and a large number of new high precision measurements suggested the need to develop an improved and more capable model. In this work, the BON2020 model is described. The new model relies on daily integral flux from the Advanced Composition Explorer Cosmic Ray Isotope Spectrometer (ACE/CRIS) to describe solar activity. For time periods not covered by ACE/CRIS, the updated international SSN database is used. Parameters in the new model are calibrated to available data, which includes the new Alpha Magnetic Spectrometer (AMS-02) and Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) high-precision measurements. It is found that the BON2020 model is a significant improvement over BON2014. Systematic errors associated with BON2014 have been removed. The average relative error of the BON2020 model compared to all available measurements is found to be <1%, and BON2020 is found to be within ±15% of a large fraction of the available measurements (26,269 of 27,646 → 95%).

Slaba, Tony C.↗

NASA NTRS2020

The Badhwar-O'Neill 2020 Model

The Badhwar-O’Neill (BON) model has been used for some time to describe the galactic cosmic ray (GCR) environment encountered in deep space by astronauts and sensitive electronics. The most recent version of the model, BON2014, was calibrated to available measurements to reduce model errors for particles and energies of significance to astronaut exposure. Although subsequent studies showed the model to be reasonably accurate for such applications, modifications to the sunspot number (SSN) classification system and a large number of new high-precision measurements suggested the need to develop an improved and more capable model. In this work, the BON2020 model is described. The new model relies on daily integral flux from the Advanced Composition Explorer Cosmic Ray Isotope Spectrometer (ACE/CRIS) to describe solar activity. For time periods not covered by ACE/CRIS, the updated international SSN database is used. Parameters in the new model are calibrated to available data, which includes the new Alpha Magnetic Spectrometer (AMS-02) and Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) high-precision measurements. It is found that the BON2020 model is a significant improvement over BON2014. Systematic errors associated with BON2014 have been removed. The average relative error of the BON2020 model compared to all available measurements is found to be <1%, and BON2020 is found to be within +15% of a large fraction of the available measurements (26,269 of 27,646  95%.

T C Slaba↗

NASA NTRS2011

Effects of Tropospheric Spatio-Temporal Correlated Noise on the Analysis of Space Geodetic Data

The standard VLBI analysis models measurement noise as purely thermal errors modeled according to uncorrelated Gaussian distributions. As the price of recording bits steadily decreases, thermal errors will soon no longer dominate. It is therefore expected that troposphere and instrumentation/clock errors will increasingly become more dominant. Given that both of these errors have correlated spectra, properly modeling the error distributions will become more relevant for optimal analysis. This paper will discuss the advantages of including the correlations between tropospheric delays using a Kolmogorov spectrum and the frozen ow model pioneered by Treuhaft and Lanyi. We will show examples of applying these correlated noise spectra to the weighting of VLBI data analysis.

thermal noise↗

NASA NTRS1986

Tracking and Data Relay Satellite (TDRS) tracking and orbit determination

Analysis results were derived using the Goddard Trajectory Determination System to determine the orbit of TDRS-East. A batch weighted least-squares algorithm is used to fit the Bilateration Ranging Transponder system tracking measurements. Six state parameters (position and velocity components) and the solar radiation pressure coefficient are estimated. The solutions are obtained by fitting the data over 34 hr arcs that overlap by 10 hr. Ephemerides are generated over the 34 hr arcs and then compared over the overlapping regions. The position differences indicate the consistency of the solutions and give a measure of their accuracy. The effect of the modeling of perturbative forces (solar radiation pressure, nonspherical geopotential field) on these results are studied. Error analysis is performed using the ORAN program to estimate the effect of force-model errors and measurement-related errors on the TDRS-East ephemeris.

Teles, J.↗